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    GATE DA Question Papers 2026 - Check Data Science and AI Questions with Solutions PDF

    GATE DA Question Papers 2026 - Check Data Science and AI Questions with Solutions PDF

    Shivani PooniaUpdated on 19 Mar 2026, 07:21 PM IST

    GATE DA Question Papers with Solutions 2026 - IIT Guwahati conducted the GATE 2026 DA exam on February 15, 2026, in online mode. Candidates who attempted the GATE DA 2026 paper can now check the officially released Master Question Paper, Candidate Response, and Master Answer Key available on the GOAPS portal by Indian Institute of Technology Guwahati, along with detailed exam analysis provided in this article. These are memory-based questions and allow candidates to determine their performance in the GATE exam. The unofficial GATE 2026 questions have been updated here. Hence, candidates can review these questions to know the difficulty level of the paper.

    GATE DA Question Papers 2026 - Check Data Science and AI Questions with Solutions PDF
    GATE DA Question Papers with Solutions 2026

    This article provides the direct link to download the GATE Data Science and Artificial Intelligence Question Papers with Answers PDF. The Subject-Wise weightage and the detailed exam pattern for the GATE 2026 Exam can also be found in this article below. Solving the GATE 2026 DA question paper plays a very important role in the preparation. Doing this helps candidates become familiar with the GATE 2026 Exam. Whether you are reviewing your performance or preparing for the next cycle, having access to the GATE 2026 DA Question Paper With Solutions is the first step toward strategic preparation. The GATE 2026 results were officially announced today on the official website of IIT Guwahati.

    GATE Data Science and Artificial Intelligence Paper: Overview

    The GATE DA question paper is designed to check a candidate's knowledge in data science and AI. Unlike the general engineering papers, the DA paper requires a strong knowledge of mathematical concepts and programming logic. Understanding the structure is important before you attempt the GATE 2026 DA Question Paper. For better understanding, you can refer to the GATE 2026 Exam Pattern. Find the exam details below:

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    Paper NameData Science and Artificial Intelligence
    Paper CodeDA
    Conducting BodyIIT Guwahati
    Mode of ExaminationComputer-Based
    Total Marks100
    Duration3 Hours
    Section-Wise WeightageGeneral Aptitude: 15 Marks
    Subject Questions: 85 Marks
    Question TypesMCQs, MSQs, NATs
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    GATE 2026 February 15 DA Memory-Based Questions

    GATE 2026 DA Analysis

    • The overall difficulty level of the exam was moderate to tough.
    • Aptitude was on the easier side.
    • Engineering mathematics was found to be on the moderate to difficult side.
    • Probability and statistics questions were trickier and on the difficult side.
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    GATE 2026 DA Paper: Question Breakdown

    • Types of Questions

      Number of questions

      Multiple choice questions(MCQs)

      33

      Multiple select questions(MSQs)

      13

      Numerical type questions(NATs)

      19

    Memory-Based Question:

    Question 1: Which of the following is not an uninformed search?

    (a)DFS

    (b)BFS

    (c)A*

    (d)DLS

    Question 2: If xyz is a three-digit number and the product of its digits is 70, then find the sum of the digits.

    (a) 16

    (b) 14

    (c) 18

    (d) 12

    Question 3:Consider the ER model:

    E1 (A11, A12, A13)

    E2 (A21, A22, A23)

    Where:

    - A21 is the key of E2

    - A22 is a multivalued attribute

    - R12 is a many-to-many relationship between E1 and E2

    Find the minimum number of relations required to convert this ER model into tables.

    Question 4: Consider a binary tree whose preorder traversal is

    P, Q, S, E, R, F, G

    And inorder traversal is

    S, Q, E, P, F, R, G

    Which of the followinng statement is correct?

    (a)Node Q has only one child

    (b)Postorder traversal is SEQFGRP

    (c)P is the root of the tree

    (d)The left subtree of node R contains node G

    Question 5: In a data warehouse, a 3D cube initially has dimensions: Product type, Month, and Country. After visualization, the dimension Country is refined into State. Which OLAP operation is performed?

    (a) Slicing

    (b) Dicing

    (c) Rollup

    (d) drill down

    Question 6: Find the maximum number of node pointers that can be used, given:

    Node size = 4096 B Node pointer = 10 B Search key = 11 B Record pointer = 12 B

    Question 7: Given the Python code:

    def fun(L, i=0):

    if i >= len(L) - 1:

    return 0

    if L[i] > L[i+1]:

    L[i+1], L[i] = L[i], L[i+1]

    L[i+1], L[i] = L[i], L[i+1] )

    else:

    return fun(L, i+1)

    data = [5, 3, 4, 1, 2]

    count = 0

    for _ in range(len(data)):

    count += fun(data)

    print(count)

    Question 8: Consider two relations R(A, B) and S(E, C). A is the primary key, and E is a FK referring to A. Which of the following operations never violate FK constraint?

    (a) Insert in R

    (b) Delete from S

    (c) Delete from R

    (d) Insert in S

    Download GATE Data Science and Artificial Intelligence Question Paper 2026

    Below we have provided the link to the GATE 2026 official question paper:


    GATE DA Subject Wise Weightage 2026

    You don’t need to invest equal time on every subject. Some subjects, like Probability and Machine Learning, are high-scoring, while others might take a lot of your time for only a few marks. Knowing the weightage helps you decide which subject to prioritize. For a better understanding of the topics, you can refer to the detailed GATE 2026 DA Syllabus.

    Chapter Name

    2025

    2024

    Total

    Percentage Distribution

    Aptitude

    10

    10

    20

    15.38%

    Artificial Intelligence(AI)

    4

    7

    11

    8.46%

    Calculus and Optimization

    6

    5

    11

    8.46%

    Database Management and Warehousing

    7

    4

    11

    8.46%

    Linear Algebra

    8

    6

    14

    10.77%

    Machine Learning

    9

    10

    19

    14.62%

    Probability and Statistics

    12

    10

    22

    16.92%

    Programming, Data Structures, and Algorithms

    9

    13

    22

    16.92%

    Total

    65

    65

    130

    100.00%

    Important Topics in GATE DA 2026

    The GATE DA 2026 Syllabus is very vast; it is very difficult to master every topic. So instead of going the hard way, we will go the smart way. The secret to getting a high rank lies in identifying the High-Weightage topics. Here are some of the most important topics for GATE DA 2026:

    Chapter Name

    Topic Name

    Count

    Aptitude

    Dice folding and visualization

    2


    Geometry – cross-section visualization

    2


    Graph coloring (minimum colors)

    2


    Inference from the passage

    2


    Infinite series sum

    2


    Permutations – divisibility

    1


    Permutations – Divisibility rule

    1


    Pie chart – percentage calculation

    2


    Probability of combinations (girls/boys)

    1


    Profit/Interest calculation (returns)

    2


    Verbal analogy

    2

    Aptitude Total


    19

    Artificial Intelligence

    AI – Adversarial search (alpha-beta pruning)

    1


    AI – Heuristic admissibility (h1, h2)

    1


    AI – Search strategy (A*) and heuristic admissibility

    1


    Alpha-beta pruning in adversarial search

    1


    Bayesian network – conditional independence

    1


    Bayesian network – joint probability computation

    1


    BFS vs DFS – state expansion count

    1


    Logic representation – rugby and round balls

    1


    Neural network – weight equivalence

    1


    Propositional logic – tautology identification

    1

    Artificial Intelligence


    11

    Calculus and Optimization

    Function continuity and differentiability (piecewise)

    1


    Limits and logarithmic expansion

    1


    Limits and logarithmic expansions

    1


    Local maxima/minima)

    1


    Local maxima/minima of a quartic polynomial

    1


    Logistic function derivative (0.4 value)

    1


    Optimization – function continuity and differentiability

    1


    Optimization – local minima (2nd derivative test)

    2


    Optimization – Taylor series and limits

    1

    Calculus and Optimization Total


    10

    Database Management and Warehousing

    ER model – relational schema (DB constraints)

    1


    Functional dependencies (DB)

    1


    Functional dependencies (derivable attributes)

    1


    Normalization & z-score

    1


    Relational algebra – ensuring team members in defender/forward

    1


    Relational algebra – set operations (Team/Defender)

    1


    SQL – Index optimization (hash vs B+)

    1


    SQL indexing optimization (hash vs B+)

    1


    SQL query result count (joins with conditions)

    1

    Database Management and Warehousing Total


    9

    Linear Algebra

    Determinant of M2+12MM^2+12M

    1


    Eigenvalues and matrix properties

    1


    Eigenvalues and signs of matrix

    1


    Eigenvalues of matrices

    1


    Eigenvalues, determinant, and matrix property

    1


    Matrix rank and nullity (subspaces)

    1


    Matrix solution scenarios (unique/infinite/none)

    1


    Matrix solutions (unique/infinite/no solutions)

    1


    Projection matrix properties

    2


    Python recursion & tree traversal

    1


    Singular values and sum

    1


    Singular values and their sum (SVD)

    1


    Subspaces of R3R^3

    1


    Subspaces of R3R^3R3

    1


    Vector subspace properties

    1

    Linear Algebra Total


    16

    Machine Learning

    Clustering – single linkage algorithm

    2


    Clustering – single linkage algorithm

    2


    Fisher Linear Discriminant (between/within scatter matrices)

    1


    k-means clustering – point assignment

    1


    k-means clustering properties

    2


    k-NN classifier (minimum k for classification)

    1


    ML – Linear separability (2D datasets)

    1


    ML – Linear separability of datasets

    3


    Naive Bayes – number of parameters estimation

    1


    Neural network – weight equivalence (ReLU)

    1


    PCA, Naive Bayes, Logistic regression (classification of models)

    1


    SVM – support vectors

    1

    Machine Learning Total


    17

    Probability and Statistics

    Binary search recurrence relation

    1


    Covariance between random variables

    1


    Dynamic programming (prefix computation)

    1


    Expected throws until two consecutive even outcomes

    1


    Logic – Propositional representation (balls/rugby)

    1


    Poisson distribution & Normal distribution properties

    2


    Probability – Bayes theorem

    2


    Probability – conditional expectation and variance

    1


    Probability – conditional/joint events

    3


    Probability – event intersection (T ∩ S)

    1


    Probability – expected value (die throws)

    1


    Probability – exponential distribution parameter

    2


    Probability – joint PDF and expectation

    2


    Probability – uniform distribution (X, Y)

    1


    Probability – uniform distributions

    1


    Probability – z-score normalization

    1


    Probability of combinations (girls/boys)

    1


    Python list reverse (recursion)

    1


    Sample mean update with new data

    1


    Sorting algorithms – bubble/insertion/selection passes

    1

    Probability and Statistics Total


    26


    AI – Heuristic admissibility (h1, h2)

    1


    Array prefix computation (dynamic programming)

    1


    Bayesian network joint probability

    1


    Binary search comparisons recurrence

    1


    Binary search complexity analysis

    1


    Binary tree node relationships (height, leaves)

    1


    Binary tree properties (height, nodes)

    1


    Covariance between random variables

    1


    DFS edge classification (tree/cross/back)

    2


    Double-ended queue operations (insert/remove)

    1


    k-NN classifier (minimum k for classification)

    1


    Python list reverse using recursion

    1


    Python recursion – counting tree nodes

    1


    Quicksort – swaps count

    1


    Relational algebra – SQL tuple verification

    1


    Sorting algorithms – bubble/insertion/selection passes

    1


    Sorting algorithms – bubble/insertion/selection passes

    1


    Topological sort of DAG

    1


    Topological sorting in DAG

    1


    Tree traversal combinations (preorder/inorder/postorder)

    1


    Uniform hashing – expected probes

    1

    Programming, Data Structures, and Algorithms Total


    22

    Grand Total


    130

    Download GATE DA Previous Year Question Papers

    Candidates can find the previous year's GATE DA question papers in the table below.

    How to Download the GATE 2026 DA Question Paper

    IIT Guwahati has release the official GATE DA Question Paper. Candidates can follow the steps to download the question paper

    1. Log on to the GATE 2026 official website.

    2. Go to the “Downloads” section.

    3. Click the GATE 2026 DA Question Paper with Solutions PDF download link.

    4. Download the pdf and keep practicing.

    Benefits of solving the GATE DA Question Paper 2026.

    Practicing with previous years' papers plays a very important role in your preparation. To score a top rank in GATE 2026, you have to combine these papers with GATE Mock Tests 2026. Here is how focusing on the GATE 2026 DA Question Paper with Solutions PDF Free Download can be a game-changer for you:

    1. Understand the Latest Trend: AI is not static. If you look at the 2024 vs. 2025 papers, you'll see a clear shift toward practical Machine Learning from pure theory.

    2. Time Management: Three hours sounds like a long time until you're stuck on a complex DBMS query. Mocks teach you when to skip and when to commit.

    Self-Assessment: You can easily analyse your weak areas in topics like Machine Learning and DBMS when you solve the GATE Data Science and Artificial Intelligence Question Papers with Answers PDF.

    Frequently Asked Questions (FAQs)

    Q: Is there any age limit to appear in the GATE Exam?
    A:

    No, there is no age limit to appear for the exam.

    Q: Is there negative marking in the GATE Exam?
    A:

    Yes, there is negative marking in the GATE exam. Marking Scheme GATE 2026: Here, you will get the detailed information about the marking scheme in the GATE exam.

    Q: What is the syllabus for the GATE DA Exam 2026?
    A:

    The GATE Data Science and AI syllabus 2026 includes subjects such as Probability, Statistics, linear algebra, Algorithm, Programming, Data Structures, DBMS, and Machine learning. Please refer to the GATE DA Syllabus 2026 for a deeper understanding.

    Q: What type of questions are asked in the GATE 2026 DA Exam?
    A:

    There will be three types of questions in GATE DA 2026, Multiple choice questions(MCQs), multiple-selection questions(MSQs), and numerical-answer-type questions(NATs).

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    Questions related to GATE

    On Question asked by student community

    Have a question related to GATE ?

    Hello,

    GATE 2026 score of 320 (OBC, AIR 1633 in XE), admission to top IITs will be difficult. However, you may get opportunities in newer IITs or lower-demand branches in later or spot rounds. You have better chances in NITs, IIITs, and state universities through CCMT counseling. PSU opportunities are

    Every PSU has different cut-off marks and eligibility. Hence, it is suggested to apply for PSU after checking the eligibility. Also, they do not reveal the marks of the finally selected candidates. So, wait for the final merit list.

    Yes. You can. GATE eligibility specifies that students in their 3rd year and above can appear for the exam. Since the results are valid for 3 years, you can use the same for your admissions.